Gender

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Function

To begin, let’s explore the function we created to run all of this statistical analysis:

mysubsetMDS <- function(x){
  mysubset <- df %>% 
    select(starts_with(x))
  
  meta <- metaMDS(mysubset)
  MDS_df <- data.frame(MDS1=meta$points[,1],MDS2=meta$points[,2]) %>%
    cbind(demo)
  return(MDS_df)
}

In English, this function allows us to run the MDS according to each subset of demographic and each subset of question type that we want. Obviously, in this page, we are exploring Gender.

Question Types

Science Identity

Let’s explore the science identity subset of questions first. Running our function we created and plotting it, we are left with this image of the plot:

This is great and all, but let’s run an adonis test to see if there is a significant difference in how different genders responded to science identity questions:

## 
## Call:
## adonis(formula = si ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$gender   4    0.2514 0.062850  1.9936 0.02532   0.13
## Residuals   307    9.6785 0.031526         0.97468       
## Total       311    9.9299                  1.00000

This shows that the differences in answers are not significant, according to gender.

Career Motivation

Next, let’s look into Carer Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = cm ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$gender   4    0.0785 0.019613 0.38219 0.00495  0.678
## Residuals   307   15.7544 0.051317         0.99505       
## Total       311   15.8329                  1.00000

This shows that the differences in answers are not significant, according to gender.

Intrinsic Motivation

Now, Intrinsic Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = im ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$gender   4    0.0468 0.011706 0.41002 0.00531   0.63
## Residuals   307    8.7651 0.028551         0.99469       
## Total       311    8.8119                  1.00000

This shows that the differences in answers are not significant, according to gender.

Self-Determination

Now, Self-Determination:

And an Adonis test:

## 
## Call:
## adonis(formula = sd ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$gender   4    0.0500 0.012490 0.50676 0.00656  0.631
## Residuals   307    7.5669 0.024648         0.99344       
## Total       311    7.6168                  1.00000

This shows that the differences in answers are not significant, according to gender.

Self-Efficacy

Now, Self-Efficacy:

And an Adonis test:

## 
## Call:
## adonis(formula = se ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$gender   4    0.0710 0.017760 0.64639 0.00835  0.428
## Residuals   307    8.4349 0.027475         0.99165       
## Total       311    8.5059                  1.00000

This shows that the differences in answers are not significant, according to gender.

Grade Motivation

Now, Grade Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = gm ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$gender   4    0.0471 0.011778 0.52835 0.00684  0.344
## Residuals   307    6.8438 0.022292         0.99316       
## Total       311    6.8909                  1.00000

This shows that the differences in answers are not significant, according to gender.

Competency in Science

Now, Competency in Science:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comp ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)  
## demo$gender   4    0.3053 0.076316  2.2316 0.02825  0.055 .
## Residuals   307   10.4987 0.034198         0.97175         
## Total       311   10.8040                  1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This shows that the differences in answers are not significant, according to gender.

Personal Community Orientation

Now, Personal Community Orientation:

And an Adonis test:

## 
## Call:
## adonis(formula = per_comm_orient ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs   MeanSqs F.Model      R2 Pr(>F)
## demo$gender   4   0.01767 0.0044169 0.49726 0.00644   0.63
## Residuals   307   2.72691 0.0088825         0.99356       
## Total       311   2.74458                   1.00000

This shows that the differences in answers are not significant, according to gender.

Science Community Orientation

Now, Competency in Science:

Well crap, something is broke there. Let’s try running a different form of MDS, giving us a similar analysis in a different way. We can interpret this data similarly to how we did before:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comm_orient ~ demo$gender) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs   MeanSqs F.Model      R2 Pr(>F)
## demo$gender   4    0.0153 0.0038188 0.25611 0.00333   0.91
## Residuals   307    4.5777 0.0149110         0.99667       
## Total       311    4.5929                   1.00000

This shows that the differences in answers are not significant, according to major.

Conclusion

Ultimately, what we can understand from all of this is that gender does not have a significant influence on the questions that were asked.

In summary, let’s take a look at the running list we are making of which questions were significant and not according to demographics:

NEXT